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However, prompts designed manually or generated by large language models may not effectively capture key discriminative visual features. In addition, pre-trained VLMs may not align images and text well at a fine-grained level. To address these two issues, we propose an attention-enhanced cross-modality alignment network, which includes an adaptive channel attention (ACA) module and a cross-modal measurement (CMM) module. The ACA module adapts the existing efficient channel attention to highlight discriminative visual and textual features. The CMM module leverages four pairs of image-text similarities across both frozen and learnable branches, improving the alignment of fine-grained discriminative visual and textual features. Experiments show that the proposed method outperforms state-of-the-art methods on two representative tasks: base-to-novel generalization and cross-dataset evaluation. Our code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/xueshaoying\/XSY_AECA.git\" ext-link-type=\"uri\">https:\/\/github.com\/xueshaoying\/XSY_AECA.git<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s10994-025-06952-5","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:51:59Z","timestamp":1770756719000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Attention-Enhanced Cross-Modality Alignment for Adapting Vision-Language Models"],"prefix":"10.1007","volume":"115","author":[{"given":"Shaoying","family":"Xue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"6952_CR1","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et\u00a0al. 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